Comparative cohort methods

Biostat 218

Author

Marc A Suchard @ UCLA

Published

February 18, 2025

1 Introduction

In these lectures we will learn about:

Using CohortMethod to perform comparative cohort studies
LSPS through FeatureExtraction and Cyclops
Evaluating objective diagnostics

2 Motivating study

What is the relative risk of gastrointestinal (GI) bleeding-related hospitalization within 30 days of celecoxib vs diclofenac treatment in patients with osteoarthritis of the knee?

Indication (I): osteoarthritis of the knee
Target (T): celecoxib first-exposure
Comparator (C): diclofenac first-exposure
Outcome (O): GI-bleed hospitalization
Time-at-risk (TAR): all time after exposure initiation
Model specification: LSPS-matched Cox proportional hazards regression

3 Exposures and outcomes

Standard vocabulary terms:

Condition: osteoarthritis of the knee (4079750)
Drug ingredient: celecoxib (1118084)
Drug ingredient: diclonefac (1124300)

library(Capr)

osteoArthritisOfKneeConceptId <- 4079750
celecoxibConceptId <- 1118084
diclofenacConceptId <- 1124300

osteoArthritisOfKnee <- cs(
  descendants(osteoArthritisOfKneeConceptId),
  name = "Osteoarthritis of knee")

attrition <- attrition(
  "prior osteoarthritis of knee" = withAll(
    atLeast(1, conditionOccurrence(osteoArthritisOfKnee), 
            duringInterval(eventStarts(-Inf, 0)))))

celecoxib <- cs(descendants(celecoxibConceptId),
                name = "Celecoxib")

diclofenac <- cs( descendants(diclofenacConceptId), 
                  name = "Diclofenac")

celecoxibCohort <- cohort(
  entry = entry(
    drugExposure(celecoxib, firstOccurrence()), 
    observationWindow = continuousObservation(priorDays = 365)),
  attrition = attrition,
  exit = exit(endStrategy = drugExit(celecoxib,
                                     persistenceWindow = 30,
                                     surveillanceWindow = 0)))

diclofenacCohort <- cohort(
  entry = entry(
    drugExposure(diclofenac, firstOccurrence()),
    observationWindow = continuousObservation(priorDays = 365)),
  attrition = attrition,
  exit = exit(endStrategy = drugExit(diclofenac,
                                     persistenceWindow = 30,
                                     surveillanceWindow = 0)))

# Note: this will automatically assign cohort IDs 1 and 2, respectively:
exposureCohorts <- makeCohortSet(celecoxibCohort, diclofenacCohort)

4 `PhenotypeLibrary` outcomes

The OHDSI PhenotypeLibrary provides a well-curated GI-bleed-related hospitalization phenotying algorithm

renv::install("OHDSI/PhenotypeLibrary")

library(PhenotypeLibrary)
outcomeCohorts <- getPlCohortDefinitionSet(77) # GI bleed

allCohorts <- dplyr::bind_rows(outcomeCohorts, exposureCohorts)
allCohorts

# A tibble: 3 × 4
  cohortId cohortName                                         json         sql  
     <dbl> <chr>                                              <chr>        <chr>
1       77 Gastrointestinal bleeding with inpatient admission "{\n\t\"cdm… "CRE…
2        1 celecoxibCohort                                    "{\"Concept… "CRE…
3        2 diclofenacCohort                                   "{\"Concept… "CRE…

5 Instantiate cohorts in data source

Use the Eumonia data source to demonstrate (actual results from Optum EHR)

library(CohortGenerator)

connectionDetails <- Eunomia::getEunomiaConnectionDetails()
cdmDatabaseSchema <- "main"
cohortDatabaseSchema <- "main"
cohortTableNames <- getCohortTableNames(cohortTable = "my_cohorts")

createCohortTables(connectionDetails = connectionDetails,
                   cohortDatabaseSchema = cohortDatabaseSchema,
                   cohortTableNames = cohortTableNames) 

generateCohortSet(connectionDetails = connectionDetails, 
                  cdmDatabaseSchema = cdmDatabaseSchema,
                  cohortDatabaseSchema = cohortDatabaseSchema,
                  cohortTableNames = cohortTableNames,
                  cohortDefinitionSet = allCohorts)

getCohortCounts(connectionDetails = connectionDetails,
                cohortDatabaseSchema = cohortDatabaseSchema,
                cohortTable = cohortTableNames$cohortTable)

  cohortId cohortEntries cohortSubjects
1       77           391            391

Notice the limitations of synthetic CDMs

What happened to the exposure cohorts? (missing indication)

6 Data pull

# Define which types of covariates must be constructed:
covSettings <- createDefaultCovariateSettings(
  excludedCovariateConceptIds = c(diclofenacConceptId, celecoxibConceptId),
  addDescendantsToExclude = TRUE)

# Pull data (no need to run)
cohortMethodData <- getDbCohortMethodData(
  connectionDetails = connectionDetails, 
  cdmDatabaseSchema = cdmDatabaseSchema,
  targetId = 1,
  comparatorId = 2,
  outcomeIds = 77,
  firstExposureOnly = FALSE,
  removeDuplicateSubjects = "keep all",
  restrictToCommonPeriod = FALSE,
  washoutPeriod = 0,
  exposureDatabaseSchema = cohortDatabaseSchema,
  exposureTable = cohortTableNames$cohortTable,
  outcomeDatabaseSchema = cohortDatabaseSchema,
  outcomeTable = cohortTableNames$cohortTable,
  covariateSettings = covSettings)

7 Simulating patient-level data from a shareable profile

J&J has kindly shared a profile of these cohorts from the Optum EHR data source (contains no patient-level information) for synthetic tutorial purposes

Download cohortMethodDataSimulationProfile
Copy into ucla-biostat-218/data for uniform access in class

To load profile and simulate cohortMethodData object

library(CohortMethod)

simulationProfile <- readRDS(
  file.path(getwd(), "data", 
            "cohortMethodDataSimulationProfile.rds"))

# Population sizes used to create profile
simulationProfile$metaData$attrition %>% 
  dplyr::select(description, targetPersons, comparatorPersons)

# A tibble: 1 × 3
  description      targetPersons comparatorPersons
  <chr>                    <int>             <int>
1 Original cohorts        116987            185248

simulationProfile$metaData$populationSize

[1] 302235

cohortMethodData <- simulateCohortMethodData(
  profile = simulationProfile,
  n = 10000             # for demonstration purposes
)

summary(cohortMethodData)

CohortMethodData object summary

Target cohort ID: 1
Comparator cohort ID: 2
Outcome cohort ID(s): 77

Target persons: 4185
Comparator persons: 5815

Outcome counts:
   Event count Person count
77         486          464

Covariates:
Number of covariates: 90758
Number of non-zero covariate values: 5012372

Important

Creating a cohortMethodData object from a remote DB takes considerable time; please remember to save object locally.

Local back-end is Andromeda

saveCohortMethodData(cohortMethodData, "coxibVsNonselVsGiBleed.zip")

To use this CohortMethodData object, you will have to load it from file (using loadCohortMethodData).

# Reload into memory for use
cohortMethodData <- loadCohortMethodData("coxibVsNonselVsGiBleed.zip")

8 Definng the study population

studyPop <- createStudyPopulation(
  cohortMethodData = cohortMethodData,
  outcomeId = 77,
  firstExposureOnly = FALSE,        # See note
  restrictToCommonPeriod = FALSE,   # See note
  washoutPeriod = 0,                # See note
  removeDuplicateSubjects = "keep all",
  removeSubjectsWithPriorOutcome = TRUE,
  minDaysAtRisk = 1,
  riskWindowStart = 0,
  startAnchor = "cohort start",
  riskWindowEnd = 30,
  endAnchor = "cohort end"
)

Study population has 9545 rows

Note

These options could have defined in

the cohorts (fastest, least re-usable)
getDbCohortMethodData() (middle-ground, what we did)
createStudyPopulation() (slowest, most re-usable)

DT::datatable(getAttritionTable(studyPop))

9 Propensity scores

Building a large-scale propensity score (LSPS) model is straight-forward:

ps <- createPs(
  cohortMethodData = cohortMethodData, 
  population = studyPop,
  # excludedCovariateConceptIds = c(diclofenacConceptId, celecoxibConceptId),
  control = Cyclops::createControl(
    seed = 1,      # reproducibility of CV
    threads = 4))  # multicore parallelization

excludeCovariateIds parameter is not needed; exposure variables removed in data fetch

Uses Cyclops to efficiently fit large-scale regularized logistic regression

Supports SIMD / multicore / GPU parallelization

Even with parallelization, LSPS models can take a long time. So remember to save the result locally

saveRDS(ps, "ps_coxibVsNonselVsGiBleed.rds")

ps <- readRDS("ps_coxibVsNonselVsGiBleed.rds")

10 Propensity score diagnostics

Compute the area under the receiver-operator curve (AUC) for treatment assignment

computePsAuc(ps)

[1] 0.7896953

Plot the propensity score distribution (on the preference-score scale)

plotPs(ps, 
       scale = "preference", 
       showCountsLabel = TRUE, 
       showAucLabel = TRUE, 
       showEquiposeLabel = TRUE)

11 Propensity score diagnostics

Inspect the fitted model by showing covariates with non-0 coefficients

possibly identify drugs of interest that we forget to exclude, or
other instrumental variables

Remember

we used cross-validated \(L_1\) regularized regression

most coefficient will shrink to 0

DT::datatable(getPsModel(ps, cohortMethodData) %>%
                mutate(absCoef = abs(coefficient)) %>%
                select(absCoef, coefficient, covariateName)) %>%
   DT::formatRound(columns=c("absCoef", "coefficient"), digits=3)

12 Propensity score diagnostics

Inspect empirical equipoise

computeEquipoise(ps)

[1] 0.8297805

Tip

A low equipoise (not seen here) indicates little overlap between T and C populations

13 Using propensity scores

Match, stratify or weigh our population

CohortMethod also supports “trimming” to equipoise (less studied)

Stratification:

stratifiedPop <- stratifyByPs(ps, numberOfStrata = 5)
plotPs(stratifiedPop, ps, scale = "preference")

Matching:

  matchedPop <- matchOnPs(ps, 
                          caliper = 0.2, 
                          caliperScale = "standardized logit", 
                          maxRatio = 1) # 1-to-1 matching

Population size after matching is 5308

  plotPs(matchedPop, ps)

Exact matching on covariates

stratifyByPsAndCovariates(covariateIds = c(...))
matchOnPsAndCovariates(covariateIds = c(...))

See the effect of matching on the population

DT::datatable(getAttritionTable(matchedPop))

14 Attrition diagram

drawAttritionDiagram(matchedPop)

Important

A little broken when injecting a simulation in the middle

Who is going to file a github issue and pull-request fix?

15 Evaluating covariate balance

Compute covariate balance before and after PS adjustment

to check that cohorts are more / sufficiently comparable

balance <- computeCovariateBalance(matchedPop, cohortMethodData)
plotCovariateBalanceScatterPlot(balance, 
                                showCovariateCountLabel = TRUE, 
                                showMaxLabel = TRUE)

Warning: Removed 20322 rows containing missing values or values outside the scale range
(`geom_point()`).

Simulation effect

Ellipsoid (most covariates are independent \(\ldots\) unlike reality)

From the original patient cohorts

16 Evaluating covariate balance

plotCovariateBalanceOfTopVariables(balance)

17 Reporting population characteristics

Most comparative cohort studies report select population characteristics before and after PS adjustment

DT::datatable(createCmTable1(balance))

18 Generalizability

PS adjustment \(\rightarrow\) make T and C more comparable

Consequence: modified population is less similar to starting data source
How different? And in what ways?

DT::datatable(getGeneralizabilityTable(balance))

Warning in instance$preRenderHook(instance): It seems your data is too big for
client-side DataTables. You may consider server-side processing:
https://rstudio.github.io/DT/server.html

Note

PS matching suggests an average treatment effect in the treated (ATT) analysis. So, getGeneralizibilityTable() automatically selected the T cohort for evaluation.

19 Follow-up and power

Minimum detectable relative risk (MDRR) reports a relative risk (under a simple Poisson model) for which there is >80% power to detect

computeMdrr(
  population = studyPop, # Should also compute under matchedPop
  modelType = "cox",
  alpha = 0.05,
  power = 0.8,
  twoSided = TRUE
)

  targetPersons comparatorPersons targetExposures comparatorExposures
1          3993              5552            3993                5552
  targetDays comparatorDays totalOutcomes     mdrr        se
1     293882         414637            62 2.057084 0.2574576

20 Follow-up and power

Follow-up time distribution statistics

getFollowUpDistribution(population = matchedPop)

  100% 75% 50% 25%  0% Treatment
1    2  21  50 100 492         1
2    2  24  54 105 596         0

plotFollowUpDistribution(population = matchedPop)

Note

Simulated time-at-risk is exponentially distribution

21 Kaplan-Meier plot

plotKaplanMeier(matchedPop)

TableGrob (2 x 1) "arrange": 2 grobs
  z     cells    name           grob
1 1 (1-1,1-1) arrange gtable[layout]
2 2 (2-2,1-1) arrange gtable[layout]

Note

Plot will automatically adjust for any stratification, matching, etc.

22 Fitting the outcome model

Using a Cox proportional hazards model

univariate: treatment-effect only

outcomeModel <- fitOutcomeModel(population = matchedPop,
                                modelType = "cox")

Using prior: None 
Using 1 thread(s)
Using 1 thread(s)
Using 1 thread(s)
Using 1 thread(s)
Using 1 thread(s)
Using 1 thread(s)

Outcome model fitting status is: OK

outcomeModel

Model type: cox
Stratified: FALSE
Use covariates: FALSE
Use inverse probability of treatment weighting: FALSE
Target estimand: att
Status: OK

          Estimate lower .95 upper .95   logRr seLogRr
treatment  1.18794   0.67710   2.09780 0.17222  0.2885

23 Fitting the outcome model

Adding interaction terms to the outcome model

interactionCovariateIds <- c(8532001) 
# 8532001 = Female
outcomeModel <- fitOutcomeModel(population = matchedPop,
                                cohortMethodData = cohortMethodData,
                                modelType = "cox",
                                interactionCovariateIds = interactionCovariateIds)

Using prior: None
Using 1 thread(s)
Using 1 thread(s)
Using 1 thread(s)
Using 1 thread(s)
Using 1 thread(s)
Using 1 thread(s)
Using 1 thread(s)
Using 1 thread(s)
Using 1 thread(s)
Using 1 thread(s)
Using 1 thread(s)

Outcome model fitting status is: OK

outcomeModel

Model type: cox
Stratified: FALSE
Use covariates: FALSE
Use inverse probability of treatment weighting: FALSE
Target estimand: att
Status: OK

                             Estimate lower .95 upper .95     logRr seLogRr
treatment                    2.345935  0.853195  7.446056  0.852684  0.5527
treatment * gender = FEMALE  0.370318  0.097754  1.260916 -0.993392  0.6523

Tip

Include gender main-effect as well via includeCovariateIds = c(interactionCovariateIds)

24 Multiple TCOs

CohortMethod has been finely tuned to efficiently execute across multiple

Targets (T)
Comparators (C)
Outcomes (O) – think: negative control outcomes (NCOs)
Analyses (A) – think: TARs, matching vs stratification

by caching intermediate study artifacts

For an example, please see Running multiple analyses vignette

25 Including negative control outcomes

Define NCOs through condition_occurrence concept IDs

Hypothyroidism (140673)

negativeControlIds <- c(29735, 140673, 197494,
                        198185, 198199, 200528, 257315,
                        314658, 317376, 321319, 380731,
                        432661, 432867, 433516, 433701,
                        433753, 435140, 435459, 435524,
                        435783, 436665, 436676, 442619,
                        444252, 444429, 4131756, 4134120,
                        4134454, 4152280, 4165112, 4174262,
                        4182210, 4270490, 4286201, 4289933)
negativeControlCohorts <- tibble(
  cohortId = negativeControlIds,
  cohortName = sprintf("Negative control %d", negativeControlIds), 
  outcomeConceptId = negativeControlIds
)

generateNegativeControlOutcomeCohorts(
  connectionDetails = connectionDetails,
  cdmDatabaseSchema = cdmDatabaseSchema, 
  cohortDatabaseSchema = cohortDatabaseSchema,
  cohortTable = cohortTableNames$cohortTable, 
  negativeControlOutcomeCohortSet = negativeControlCohorts
)

getCohortCounts(connectionDetails = connectionDetails,
                cohortDatabaseSchema = cohortDatabaseSchema,
                cohortTable = cohortTableNames$cohortTable)

  cohortId cohortEntries cohortSubjects
1       77           391            391
2   140673            31             31
3   198199             9              9

26 Acknowledging the community

Considerable work has been dedicated to provide the CohortMethod and Cyclops packages

citation("CohortMethod")

To cite package 'CohortMethod' in publications use:

  Schuemie M, Suchard M, Ryan P (2024). _CohortMethod: New-User Cohort
  Method with Large Scale Propensity and Outcome Models_. R package
  version 5.4.0, commit 893b5445e8a92c3a118db1b9cf92db8dbccdee39,
  <https://github.com/OHDSI/CohortMethod>.

A BibTeX entry for LaTeX users is

  @Manual{,
    title = {CohortMethod: New-User Cohort Method with Large Scale Propensity and Outcome
Models},
    author = {Martijn Schuemie and Marc Suchard and Patrick Ryan},
    year = {2024},
    note = {R package version 5.4.0, commit 893b5445e8a92c3a118db1b9cf92db8dbccdee39},
    url = {https://github.com/OHDSI/CohortMethod},
  }

citation("Cyclops")

To cite Cyclops in publications use:

  Suchard MA, Simpson SE, Zorych I, Ryan P, Madigan D (2013). "Massive
  parallelization of serial inference algorithms for complex
  generalized linear models." _ACM Transactions on Modeling and
  Computer Simulation_, *23*, 10.
  <https://dl.acm.org/doi/10.1145/2414416.2414791>.

A BibTeX entry for LaTeX users is

  @Article{,
    author = {M. A. Suchard and S. E. Simpson and I. Zorych and P. Ryan and D. Madigan},
    title = {Massive parallelization of serial inference algorithms for complex generalized linear models},
    journal = {ACM Transactions on Modeling and Computer Simulation},
    volume = {23},
    pages = {10},
    year = {2013},
    url = {https://dl.acm.org/doi/10.1145/2414416.2414791},
  }

This work is supported in part through the National Institutes of Health and the U.S. Department of Veterans Affairs

1 Introduction

2 Motivating study

3 Exposures and outcomes

4 PhenotypeLibrary outcomes

5 Instantiate cohorts in data source

6 Data pull

7 Simulating patient-level data from a shareable profile

8 Definng the study population

9 Propensity scores

10 Propensity score diagnostics

11 Propensity score diagnostics

12 Propensity score diagnostics

13 Using propensity scores

14 Attrition diagram

15 Evaluating covariate balance

16 Evaluating covariate balance

17 Reporting population characteristics

18 Generalizability

19 Follow-up and power

20 Follow-up and power

21 Kaplan-Meier plot

22 Fitting the outcome model

23 Fitting the outcome model

24 Multiple TCOs

25 Including negative control outcomes

26 Acknowledging the community

4 `PhenotypeLibrary` outcomes